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Statistical data analysis using SAS: intermediate statistical methods

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basi...

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Detalles Bibliográficos
Autores principales: Marasinghe, Mervyn G, Koehler, Kenneth J
Lenguaje:eng
Publicado: Springer 2018
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-69239-5
http://cds.cern.ch/record/2316209
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author Marasinghe, Mervyn G
Koehler, Kenneth J
author_facet Marasinghe, Mervyn G
Koehler, Kenneth J
author_sort Marasinghe, Mervyn G
collection CERN
description The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: •    Covers SAS v9.2 and incorporates new commands •    Uses SAS ODS (output delivery system) for reproduction of tables and graphics output •    Presents new commands needed to produce ODS output •    All chapters rewritten for clarity •    New  and updated examples throughout •    All SAS outputs are new and updated, including graphics •    More exercises and problems •    Completely new chapter on analysis of nonlinear and generalized linear models •    Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.
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spelling cern-23162092021-04-21T18:50:59Zdoi:10.1007/978-3-319-69239-5http://cds.cern.ch/record/2316209engMarasinghe, Mervyn GKoehler, Kenneth JStatistical data analysis using SAS: intermediate statistical methodsMathematical Physics and MathematicsThe aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem. New to this edition: •    Covers SAS v9.2 and incorporates new commands •    Uses SAS ODS (output delivery system) for reproduction of tables and graphics output •    Presents new commands needed to produce ODS output •    All chapters rewritten for clarity •    New  and updated examples throughout •    All SAS outputs are new and updated, including graphics •    More exercises and problems •    Completely new chapter on analysis of nonlinear and generalized linear models •    Completely new appendix Mervyn G. Marasinghe, PhD, is Associate Professor Emeritus of Statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing. Kenneth J. Koehler, PhD, is University Professor of Statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teaching.Springeroai:cds.cern.ch:23162092018
spellingShingle Mathematical Physics and Mathematics
Marasinghe, Mervyn G
Koehler, Kenneth J
Statistical data analysis using SAS: intermediate statistical methods
title Statistical data analysis using SAS: intermediate statistical methods
title_full Statistical data analysis using SAS: intermediate statistical methods
title_fullStr Statistical data analysis using SAS: intermediate statistical methods
title_full_unstemmed Statistical data analysis using SAS: intermediate statistical methods
title_short Statistical data analysis using SAS: intermediate statistical methods
title_sort statistical data analysis using sas: intermediate statistical methods
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-69239-5
http://cds.cern.ch/record/2316209
work_keys_str_mv AT marasinghemervyng statisticaldataanalysisusingsasintermediatestatisticalmethods
AT koehlerkennethj statisticaldataanalysisusingsasintermediatestatisticalmethods